library(tidyverse)
library(MouseGastrulationData)
library(SingleCellExperiment)
library(scater)
library(scran)
library(bit64)
library(patchwork)
options(digits=3)
source('~/milo/notebooks/benchmark/benchmark_utils.R')
dir.exists(figdir)
[1] FALSE
Select samples from late time points (even number of replicates)
```r
AtlasSampleMetadata %>%
filter(stage %in% c(\E7.75\, \E8.0\, \E8.25\, \E8.5\)) %>%
pull(sample)
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[1] 8 9 12 13 16 17 24 25 28 29 33 34 35 36 37
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZW1icnlvX3NjZSA8LSBFbWJyeW9BdGxhc0RhdGEodHlwZT1cXHByb2Nlc3NlZFxcLCBzYW1wbGVzID0gbGF0ZV9zYW1wbGVzKVxuXG5gYGBcbmBgYCJ9 -->
```r
```r
embryo_sce <- EmbryoAtlasData(type=\processed\, samples = late_samples)
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browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\nsee ?MouseGastrulationData and browseVignettes('MouseGastrulationData') for documentation\nloading from cache\n"} -->
snapshotDate(): 2020-10-27 see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache see ?MouseGastrulationData and browseVignettes(‘MouseGastrulationData’) for documentation loading from cache
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zarhHfqC8RbEuKF54eO94GPfGfzVjvE64Z44fmh443EnBCvG+KF54eO1wy+aiBejxAv2uQdc7QQL9o8TfHyjjlKiBeeHzpe3jFHCfHC80PHyzvmKCFeeH7oeHnHHCXEC88PHS/vmKOEeOH5oePlHXOUEC88P3S8vGOOEuKF54eO1/kR9OPnEa8b4oXnh4z39/c33J/P0jOI1w3xwvNDxtt+WXRqPhfeS7xuiBeeH/plQ5OfWuL1DfGizSrhTTT/XOS+hb4/ku45h3jBEG9V8F7/V7tFOqbPJ143xAvPDx3vOws/kvN//A5bMcQLzw8d75lv5f98/UzidUO88PzQ8V4xd3/O/MMnPkO8bogXnh863kONkbpoZMax8myJ1y/Eizar+O3hLS/Kn4jXDfHC80cBXsswjINnF99zV1LZctPSNRni9Q/xos0q4d07Y1z+3rwfKb7vrKTy0o1JeXAT8fplcjQSiU6DqvlbRqNr/BCv1xYd7yc+t+WcJ5dd0jP4mbdwV+nta0T2LSJev0Tgm5hPLDRBvcTrtUXHe1a/TBF55MQ3KQp4913XYz1yjf0f8Q0bNuyMeySb89qixcqgzZQk0KphwvMljTbTgvWmOXiRahSvxuMZcL6dnIE2k5JCq1kLnm9l0WZKkmjV8ESV1PCenZApKUlPLsUrT7eu2Pol+21XU1PTOssj9vUyGrxZQTXU+Wc4IqcBVRfv8M63qvT4K9npyM7Panibrk18/K7sjkkKXju7WnnZ4Bf8sqHOWZ8K2y0vG7y26JcNL57f858NschtpXiPLUmby39GvH4pkIxB1cKn3lnYbonXa4uC92hO8suw7Xv0f4pOiyupPLZg8UaLeH0zqa7uXLA6rg5T3k28MN5xveL7Qw3E6xu+zos2q4H3/TPn1893QrxuiBeeHy7ejnmXx2Y7IV43xAvPD/0Ltkt42VAa4oXnh44Xiecc4gVDvFXEe+8/Ea8b4oXnjxK8668mXjfEC88fJXj94zmHeMEQb1XwJu8UefSS63vKsyVevxAv2qwS3hsvMl+pb571FeJ1Q7zw/NDxTj4o37xU3jyXeN0QLzw/dLwTcvLRH4rZQLxuiBeeHzre85/ZVX9Qdv4F8bohXnh+6HjXRyML5Oh5q4jXDfHC80PHK6/tssT40X7idUO88Pzw8Sq/+k68xIsmdLzqr74TL/GiCR2v+qvvxEu8aELHq/3qO/ESL5jQ8Wq/+k68xAsmdLzar74TL/GCCR2v9qvvxEu8YELHm8+QX30nXuKF548KvEHxnEO8YIiXeJUQL9okXn0O8YIhXuJVQrxok3j1OcQLhniJVwnxok3i1ecQLxjiHRG87mpAIk9fkRqTeBtik9Eq8aLNEcLrrAYkcrh17ljEO6uCe5gT72jD66wGJMatB64Zi3id5SPqsTLxos2RuuZ18D6wTQp4j82bN2+z4ZGc5bVFi+TQpinwTq3hn+8sNBEd/vkm2sxV8PgrOKnVmF/Jk4rP9zypGRDvzlWWg7dv9erV7SmPmJbXFi1ioM2MpNGqmcPnZ7Geixcr50x0fBqdn0oZgjYrmp+B5+NPqlWVJ9V7Poj3eze1tMxZ+sZYvWw4AyvzsgFtjuxlg50xec1b0IsufkK8aHNk8BZXAxqzePk6b+3iLRvPOcQLhniJVwnxok3i1ecQLxjiJV4lxIs2iVefQ7xgiJd4lRAv2iRefQ7xgiFe4lVCvGiTePU5xAuGeIlXCfGiTeLV5xAvGOIlXiXEizaJV59DvGCIl3iVEC/aJF59DvGCIV7iVUK8aJN49TnEC4Z4iVcJ8aJN4tXnEC8Y4iVeJcSLNolXn0O8YIiXeJUQL9okXn0O8YIhXuJVQrxok3j1OcQLhniJVwnxok3i1ecQLxjiJV4lxIs2iVefQ7xgiJd4lRAv2hwjeJ3VgKyHFy1pPXD64I1Go+ANo4m3hvE6qwG9stKQbatOG7yF252Pw7rEC88fdXgTxTujm+vX2n9m9+zZ09XjkbTptUWLlUKbcelDq1kD69U5K01g5YT0ovONLNrskzhaTVpos8fMoM1+GUCrqRw8P5dGmwPSj1YznqgGAvEWb+v/8NW35++MfqSxsbHNu1wbcRaaqOw6nxl1Mdy3wJoU5kPftv/MdHR0HOr1SMb02qLFSqHNhPSj1ayB9dzPvFg5KX3ofCOLNvskgVZTgjZ7zQzaHJA4Wk3n4Pm5NNqMywBa9UYVB/EeetW+0v1M8UOelyc1dc0LrgzIa154/qi75nXwPrcwKb9aetrgnRWNRMeDXeKF5482vMXVgDY1L1526LTBy9d5xwTesvGcQ7xgiJd4lRAv2iRefQ7xgiFe4lVCvGiTePU5xAuGeIlXCfGiTeLV5xAvGOIlXiXEizaJV59DvGCIl3iVEC/aJF59DvGCIV7iVUK8aJN49TnEC4Z4iVcJ8aJN4tXnEC8Y4iVeJcSLNolXn0O8YIiXeJUQL9okXn0O8YIhXuJVQrxok3j1OcQLhniJVwnxok3i1ecQLxjiJV4lxIs2iVefQ7xgiJd4lRAv2iRefQ7xgiFe4lVCvGiTePU5xAuGeEdwQZXiG+L1C/GizRFdUKX4ZuTx5u9iHoOa06KRSHQSuFviRau1jNddUGVwXZURxuvcgb8BqTr36gfnEy9arWW8xTUp3Df9bW1tv016xMh5bdEiWazniIzi1XOHd34ymRG0mcyZaDMlGbSarWC+gTbTkkarhgXPt6oy3xtVpXi7mpqa1lkeEfHaolfBnosXaM5yqhOGd35F1So8/vDnV7LTkZ2frRTvSF82OKtO1SFVXjbwsmF04V1R+MQLVWMw827iHRN43QVViuuqjDje7u76OnTdnml1dZPRvRIvWq1hvGXjOYev84IhXuJVQrxok3j1OcQLhniJVwnxok3i1ecQLxjiJV4lxIs2iVefQ7xgiJd4lRAv2iRefQ7xgiFe4lVCvGiTePU5xAuGeIlXCfGiTeLV5xAvGOIlXiXEizaJV59DvGCIl3iVEC/aJF59DvGCIV7iVUK8aJN49TnEC4Z4iVcJ8aJN4tXnEC8Y4iVeJcSLNolXn0O8YIiXeJUQL9okXn0O8YIhXuJVQrxok3j1OcQLhniJVwnxok3i1ecQLxjiJV4lxIs2xxbezV9fefuBILz1sWngEU2OjUMPnnjRJvGWz975OWn/pj/eBvzmzoX7nYP30iVetEm85XPkqwOydY0/Xvy2+hMj+P3OiZd4PQJfNmz7/OIb3rbfHv30pz+90SwbB299+Y0lcdanigBNOznBenYsC65KDm2elvNz+HwLny9Vme+50wyI97UFfbI9f9nQt3r16vZU2Tgix5ffWBIXL9C0k5E0VkylzBzaTEkWbWYF3mnORJtpfL5RnfkZeL4Fz7cMtFnJk+o9H8S7tU2k5zP+lw1R+LJhKi8beNmAVk/9smHnoqw8t8Qf76woulZldz1ul3iJ1yP4S2Wty5cHvlTG13nRJvHymxRKiBdtEq8+h3jBEC/xKiFetEm8+hziBUO8xKuEeNEm8epziBcM8RKvEuJFm8SrzyFeMMRLvEqIF20Srz6HeMEQL/EqIV60Sbz6HOIFQ7zEq4R40Sbx6nOIFwzxEq8S4kWbxKvPIV4wxEu8SogXbRKvPod4wRBvaHi98uwjcHXtTrS5v20ArT61BW3m2l5Gq39sM9Dq1l+gzUTba2h11w/QpmxqR5tvt3Wi1d/+CJ7/4G/R5httx9BqeyCq4cF71+fg6iUPos3/anwLrd52A9rMNsLOf9aYCi45WXgr2uxuhJ395KNoUz5/B9p8rRH+5PGvl8LzL2tDm7sa96HV1XODGsTrGeKF5xOvGuJFm8R7Kunpgqtv9KPNVKeJVt8+ijatzjhaTXRaaLX7bbSZ60yi1YE30KZ0HUeb2c40Wu07As8/0hfccZLpzKLVYFTDg5dhQgjxMjWbYcFbcgNq32y5aemaTHAtsWZO/s32RTd/I+jfLqdqPbxoSWvQIbh73d9yc8ubSPO55ubmBdcE7HTwGJ++IugS2anG59i7/Sm0U+S8ulXgxLrnCDhVJyqBD8qtAg+quFPg9LvVwPM/HHhLb0Dtl5duTMqDm4J7K5690v7z9S/3y+N3QdVXVhqybRVUzV2/W379GNLMZ9Oj/s3BYzzcOjfgeXarXdcHHOZgEzmvbhU5se45Ak7VYCX4QblV4EG5TeT0DzlE3/M/HHhLb0Dtl+12a9+i4F4ilcdj2l8G7FgOVfPt9Wuh6h54vp3ORQHfqCgeo3HrgWsCnme3+vriwPFuEzmvbhU8se45Cj5VTgV4UG4VeFBuEzn9MniI/ud/WC4bijegDsy+63qsR64BikU8qZs7wOrDV98eeJ4L1fZVa5fdGXQxUpz/3R1B+3SP8YFtAjzP+erLX71jyd2BX0gXdoqd13wVO7HuOQJOlVOBHlShCj2oQhM6/YOH6H/+hwPv4A2og/N064qtXwJ6Lp7uJcHfdx38zPvQt6Fq+xe6ZPvXsZ0euSH41bLCMe5cZQHPc6F69JcpeSLodeFCEzuvzjnCTqx7joBTla9gD6pQhR5UoQmd/uIhBpz/4cBbegPqoOxqBUoOnsMLdoHVQ6/az2LgIRSqLy4T6f8sNF8eC/72vnOM37uppWXO0oAXZk88nL45/v+ncJrQeT2x06AT654j4FS5FeRBDdlbwINym8jpH9xpwPkfDrylN6D2y7ElaXP5z4BiAU9mwV60+tzCpPxqKVQ1bjgsO1qQpsjywB84GXKMQZ+k3Opzd5vylL8zt4mcV7eKnFj3HAGnakgl6EEVdxr8oNwmcvoH5wec/+F5qWzoDah989iCxRuD/yXuaf7aFc1L5ZmrmpubAz6duFXZ1Lx42SGsurd1yW3+1WJTFrwSdKhDjjHoeXar5trmW5b7v1ZU3ClwXotV5MS65wg4VScqgZcNThV4UMWdBp/+E/MDzj+/ScHUbIiXqdkQL1OzIV6mZkO8TM2GeIczHX/ZEPyTx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<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZW1icnlvX3NjZVxuXG5gYGAifQ== -->
```r
embryo_sce
class: SingleCellExperiment
dim: 23434 64018
metadata(0):
assays(2): counts logcounts
rownames(23434): ENSMUSG00000051951 ENSMUSG00000102343 ... ENSMUSG00000063897
ENSMUSG00000095742
rowData names(2): ENSEMBL SYMBOL
colnames(64018): cell_7625 cell_7626 ... cell_139330 cell_139331
colData names(22): cell barcode ... synth_samples true_labels
reducedDimNames(6): pca.corrected umap ... pca_batch umap_batch
altExpNames(0):
logcounts(embryo_sce) <- log1p(counts(embryo_sce))
## Exclude zero counts genes
keep.rows <- rowSums(logcounts(embryo_sce)) != 0
embryo_sce <- embryo_sce[keep.rows, ]
dec <- modelGeneVar(embryo_sce)
hvgs <- getTopHVGs(dec, n=5000)
## Drop cells with NAs in corrected pca (low quality)
embryo_sce <- embryo_sce[,apply(reducedDim(embryo_sce, "pca.corrected"), 1, function(x) all(!is.na(x)))]
## Run UMAP (the data has been subsetted)
# embryo_sce <- scater::runPCA(embryo_sce, subset_row=hvgs)
embryo_sce <- scater::runUMAP(embryo_sce, dimred="pca.corrected")
plotReducedDim(embryo_sce, "UMAP", colour_by="celltype", text_by="celltype", point_size=0.1) +
scale_color_manual(values=EmbryoCelltypeColours) +
guides(color=guide_legend(override.aes = list(size=2)))
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Save object 4 benchmark
```r
saveRDS(embryo_sce, \/nfs/team205/ed6/data/milo_benchmark/embryo_data_bm.RDS\)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Save list of celltype names and sample by sizes
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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## Simulate ONE DA region
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<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo3LCJzaXplX2JlaGF2aW9yIjoxLCJ3aWR0aCI6N30= -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## Try one method at the time (to pick params)
<!-- ```{r} -->
<!-- embryo_sce <- readRDS("/nfs/team205/ed6/data/milo_benchmark/embryo_data_bm.RDS") -->
<!-- # Build KNN graph for smoothing -->
<!-- X_red_dim = reducedDim(embryo_sce, "pca.corrected")[,1:30] -->
<!-- graph = buildKNNGraph(t(X_red_dim), k = 15) -->
<!-- ## Simulate labels -->
<!-- seed=2022 -->
<!-- embryo_sce <- add_synthetic_labels(embryo_sce, -->
<!-- n_components = 10, -->
<!-- redDim='pca.corrected', -->
<!-- seed=seed, -->
<!-- knn_graph = graph, -->
<!-- n_replicates = 6) -->
<!-- true_labels <- ifelse(embryo_sce$Condition2_prob < 0.4, "NegLFC", ifelse(embryo_sce$Condition2_prob > 0.6, "PosLFC", "NotDA")) -->
<!-- colData(embryo_sce)[["true_labels"]] <- true_labels -->
<!-- embryo_sce <- runUMAP(embryo_sce, dimred="pca.corrected", name = 'UMAP', n_dimred=1:30) -->
<!-- ``` -->
<!-- Divide cells into true affected subpopulations: take KNN adjacency matrix, filter out edges between cells with different label, then do louvain_clustering at high res -->
<!-- ```{r} -->
<!-- embryo_sce <- cluster_synthetic_labels(embryo_sce, graph) -->
<!-- plotReducedDim(embryo_sce, dimred="UMAP", colour_by="true_labels", point_size=0.2) -->
<!-- plotReducedDim(embryo_sce, dimred="UMAP", colour_by="true_DA_clust", -->
<!-- text_by="true_DA_clust", -->
<!-- point_size=0.2) -->
<!-- ``` -->
### Cydar
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMgQ3lkYXJcbmN5ZGFyX3JlcyA8LSBydW5fY3lkYXIoZW1icnlvX3NjZSwgdG9sPTMsIGQ9MzApXG5cbmBgYCJ9 -->
```r
## Cydar
cydar_res <- run_cydar(embryo_sce, tol=3, d=30)
Pick K using within/between cluster distance
dim(as.matrix(nhoods_mat[1:3000,1:100]) %*% X_pca_ixs[1:100,])
[1] 3000 30
Compare with MELD
meld_out <- run_meld_reticulate(embryo_sce, condition_col="synth_labels", sample_col="synth_samples", reduced.dim="pca.corrected",
k=50)
embryo_sce$meld_res <- meld2output(meld_out, out_type = "labels")
plotReducedDim(embryo_sce, dimred="UMAP", colour_by="meld_res", point_size=0.2)
plotReducedDim(embryo_sce, dimred="UMAP", colour_by="true_labels", point_size=0.2)
## Make design matrix
sample_col = "synth_samples"
condition_col = "synth_labels"
batch_col = NULL
design_df <- as.tibble(colData(embryo_sce)[c(sample_col, condition_col, batch_col)]) %>%
distinct() %>%
column_to_rownames(sample_col)
if (is.null(batch_col)) {
design <- formula(paste('~', condition_col, collapse = ' '))
} else {
design <- formula(paste('~', batch_col, "+", condition_col, collapse = ' '))
}
milo_ls <- lapply(milo_ls, function(m){
## Test DA
m <- countCells(m, meta.data = data.frame(colData(m)), sample='synth_samples')
m <- calcNhoodDistance(m, d=30, reduced.dim = "pca.corrected")
DA_results <- testNhoods(m, design = design, design.df = design_df)
return(list(Milo=m, da_res=DA_results))
})
milo_outcome <- lapply(seq_along(milo_ls), function(i)
data.frame(pred=milo2output(milo_ls[[i]]$Milo, milo_ls[[i]]$da_res, out_type="labels"),
true=milo_ls$`20`$Milo$true_labels,
method=names(milo_ls)[i])
) %>%
purrr::reduce(bind_rows) %>%
calculate_outcome()
milo_outcome %>%
ggplot(aes(method, Power)) + geom_point()
milo_outcome %>%
ggplot(aes(method, TPR)) + geom_point()
milo_out2 <- run_milo(embryo_sce, condition_col="synth_labels", sample_col="synth_samples", reduced.dim="pca.corrected",
k=30, prop=0.1)
milo_out2$Milo <- buildNhoodGraph(milo_out2$Milo)
hist(rowSums(milo_out2$Milo@nhoods), breaks=100)
milo_out2$DAres %>%
ggplot(aes(logFC, - log10(SpatialFDR))) + geom_point()
plotNhoodSizeHist(milo_out2$Milo) /
plotNhoodGraphDA(milo_out2$Milo, milo_out2$DAres)
meld_res_ls <- lapply(seq(20,50, by = 10), function(k){
meld_res <- run_meld_reticulate(embryo_sce, condition_col="synth_labels", sample_col="synth_samples", reduced.dim = "pca.corrected", d=30, k=k)
meld_out <- meld2output(meld_res, out_type = "labels")
meld_out
})
lapply(meld_res_ls)
# meld_outcome <-
lapply(seq_along(meld_res_ls), function(i)
data.frame(pred=meld_res_ls[[i]],
true=embryo_sce$true_labels,
method=seq(20,50, by = 10)[i])
) %>%
purrr::reduce(bind_rows) %>%
mutate(pred=as.character(pred)) %>%
mutate(pred=case_when(pred=="PosLFC" ~ "NegLFC",
pred=="NegLFC" ~ "PosLFC",
TRUE ~ pred)) %>%
mutate(outcome=case_when(true==pred & pred!="NotDA" ~ 'TP',
true!=pred & pred!="NotDA" ~ 'FP',
true!=pred & pred=="NotDA" ~ 'FN',
true==pred & pred=="NotDA" ~ "TN"
)) %>%
group_by(method, outcome) %>%
summarise(n=n()) %>%
pivot_wider(id_cols=method, names_from=outcome, values_from=n, values_fill=0) %>%
mutate(TPR=TP/(TP+FP), FPR=FP/(TP+FP), TNR=TN/(TN+FN), FNR = FN/(FN+TP),
Power = 1 - FNR,
Accuracy = (TP + TN)/(TP + TN + FP + FN),
Recall = TP / (TP+FN)
)
(the following code is wrapped in the run_batch_benchmark.R script but I am rerunning for viz)
Visualize batch effects of different intensity
lapply(bm_sce_ls, function(x) plotReducedDim(x, dimred="umap_batch", colour_by="true_labels", point_size=0.2) +
ggtitle(paste0("Batch effect SD = ", x$norm_sd[1])))
[[1]]
[[2]]
[[3]]
[[4]]
run_batch_benchmark.RsetNames(method_colors, method_names)
milo daseq meld louvain milo_batch louvain_batch
"#1B9E77" "#D95F02" "#7570B3" "#E7298A" "#66A61E" "#E6AB02"
## Read DA predictions
prob_thresh_vec <- seq(0.5, 0.9, 0.05)
outcome_df <- lapply(seq_along(res_files_full), function(i){
print(paste("Outcome no. ", i))
benchmark_df <- read_csv(res_files_full[i])
pop_enr <- as.numeric(out_meta_df[i,"enr"])
lapply(prob_thresh_vec[prob_thresh_vec < pop_enr], function(x){
benchmark_df %>%
.outcome_by_prob(da_upper = x) %>%
mutate(DA_thresh=x) %>%
ungroup() %>%
dplyr::select(- method) %>%
bind_cols(out_meta_df[i,]) %>%
filter(DA_thresh < as.numeric(enr))
}) %>%
purrr::reduce(bind_rows)
}) %>%
purrr::reduce(bind_rows)
write_csv(outcome_df, "/nfs/team205/ed6/data/milo_benchmark/outcome_full.csv")
outcome_df <- read_csv("/nfs/team205/ed6/data/milo_benchmark/outcome_full.csv")
Motivate threshold
Breakdown by population: explain variability in MELD, population size
pop_sizes <- table(embryo_sce$celltype)
outcome_df %>%
filter(method!="milo_batch") %>%
mutate(FDR = FP/(TP+FP)) %>%
mutate(TPR=ifelse(is.nan(TPR), 0, TPR),
FPR=ifelse(is.nan(FPR), 0, FPR),
FDR=ifelse(is.nan(FDR), 0, FDR),
Precision=ifelse(is.nan(Precision), 0, Precision)) %>%
mutate(pop=str_replace(pop, "_", " ")) %>%
mutate(pop_size=pop_sizes[pop]) %>%
arrange(pop_size) %>%
mutate(pop=factor(pop, levels=unique(pop))) %>%
filter(DA_thresh >= 0.6 & batchEffect==0) %>%
ggplot(aes(pop_size, TPR, color=method)) +
geom_point(size=2, alpha=0.7) +
facet_grid(enr~., labeller="label_both") +
geom_smooth(method="lm") +
theme_bw(base_size = 16) +
scale_color_manual(values = method_colors, labels=setNames(method_labels, method_names)) +
scale_fill_manual(values = method_colors, labels=setNames(method_labels, method_names)) +
xlab("Size of DA population") +
xlim(0,7000) +
ggpubr::stat_cor(label.x.npc = 1, label.y.npc = 1, hjust = 1) +
ggsave(paste0(figdir, "pop_size.png"), height = 7, width = 8)
NA
NA
NA
Load louvain_batch
Visualize outcome in UMAP
colData(embryo_sce) <- read_csv(coldata_file) %>% column_to_rownames() %>% DataFrame()
Error in is.connection(x) : object 'coldata_file' not found
Plot benchmark design
Design matrix
p1 <- lapply(pops, function(p) plot_outcome_umap(sce, 'milo', p, 0.7, seed, batchEffect, true=TRUE, rasterize=TRUE))
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
p2 <- lapply(pops, function(p) plot_outcome_umap(sce, 'milo', p, 0.8, seed, batchEffect, true=TRUE, rasterize=TRUE))
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
p3 <- lapply(pops, function(p) plot_outcome_umap(sce, 'milo', p, 0.9, seed, batchEffect, true=TRUE, rasterize=TRUE))
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
fig <- (wrap_plots(p1) /
wrap_plots(p2) /
wrap_plots(p3)) +
plot_layout(guides="collect") &
theme(legend.position = "right")
fig +
ggsave(paste0(figdir, "bm_design_UMAPs.png"), height = 8, width = 10)
Plot batch effect
Design matrix w batch effect
coldata_file <- list.files(indir, pattern=paste0(pop,".+", "enr",enr,".+",'seed', seed, ".coldata.csv"),
full.names = TRUE)
colData(embryo_sce) <- read_csv(coldata_file) %>% column_to_rownames() %>% DataFrame()
[36m──[39m [1m[1mColumn specification[1m[22m [36m────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────[39m
cols(
.default = col_character(),
sample = [32mcol_double()[39m,
pool = [32mcol_double()[39m,
sequencing.batch = [32mcol_double()[39m,
doub.density = [32mcol_double()[39m,
doublet = [33mcol_logical()[39m,
cluster = [32mcol_double()[39m,
cluster.sub = [32mcol_double()[39m,
cluster.stage = [32mcol_double()[39m,
cluster.theiler = [32mcol_double()[39m,
stripped = [33mcol_logical()[39m,
sizeFactor = [32mcol_double()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m
)
[36mℹ[39m Use [38;5;235m[48;5;253m[38;5;235m[48;5;253m`spec()`[48;5;253m[38;5;235m[49m[39m for the full column specifications.
batch_col="synth_batches"
embryo_sce <- add_batch_effect_nonlinear(embryo_sce, dims=c(3,5), theta_deg = 30)
embryo_sce <- runUMAP(embryo_sce, dimred="pca_batch", name = 'umap_batch', n_dimred=1:30)
data.frame(reducedDim(embryo_sce, "umap_batch")) %>%
rename(UMAP1=X1, UMAP2=X2) %>%
mutate(true=embryo_sce$synth_batches) %>%
ggplot(aes(UMAP1, UMAP2, color=true)) +
geom_point(size=0.1) +
scale_color_viridis_d(name="Batch", option="cividis") +
theme_classic(base_size=18) +
guides(color=guide_legend(override.aes = list(size=2))) +
theme(axis.ticks = element_blank(),
axis.text = element_blank()) +
xlab("UMAP1") + ylab("UMAP2")
data.frame(reducedDim(embryo_sce, "UMAP")) %>%
rename(UMAP1=X1, UMAP2=X2) %>%
mutate(true=embryo_sce$synth_batches) %>%
ggplot(aes(UMAP1, UMAP2, color=true)) +
geom_point(size=0.1) +
scale_color_viridis_d(name="Batch", option="cividis") +
theme_classic(base_size=18) +
guides(color=guide_legend(override.aes = list(size=2))) +
theme(axis.ticks = element_blank(),
axis.text = element_blank()) +
xlab("UMAP1") + ylab("UMAP2")
NA
NA
Make non-linear batch effect for all populations
pop_enrs[1:3]
Error: object 'pop_enrs' not found
Running in run_DA_nonlinear.R.
Read all results
outdir <- "/nfs/team205/ed6/data/milo_benchmark/"
res_files <- list.files(outdir, pattern="NonLinear.+DAresults.+.csv")
res_files_full <- list.files(outdir, pattern="NonLinear.+DAresults.+.csv", full.names = TRUE)
## Make data frame w benchmark parameters
nonl_out_meta_df <- data.frame(file_id = str_remove_all(res_files, "benchmark_embryo_pop_|.csv")) %>%
separate(col = file_id, sep = ".DAresults.", into=c("file_id", "method")) %>%
separate(col = file_id, sep = "_enr", into=c("pop", "file_id")) %>%
separate(col = file_id, sep = "_", into=c("enr", "seed", "batchEffect")) %>%
mutate(seed=str_remove(seed, "seed"), batchEffect=str_remove(batchEffect, "batchEffect"))
## Load results
nonl_outcome_df <- lapply(seq_along(res_files_full), function(i){
print(paste("Outcome no. ", i))
benchmark_df <- read_csv(res_files_full[i]) %>%
.outcome_by_prob(da_upper = 0.6) %>%
mutate(DA_thresh=0.6) %>%
ungroup() %>%
dplyr::select(- method) %>%
bind_cols(nonl_out_meta_df[i,])
pop_enr <- as.numeric(nonl_out_meta_df[i,"enr"])
benchmark_df
}) %>%
purrr::reduce(bind_rows)
nonl_outcome_df
library(batchelor)
#
# be_sce <- be_sce_ls[[4]]
# coldata_file <- list.files(indir, pattern=paste0(pop,".+", "enr",enr,".+",'seed', seed, ".coldata.csv"),full.names = TRUE)
# colData(be_sce) <- DataFrame(read_csv(coldata_file) %>% column_to_rownames())
mnn_correct_batch_effect <- function(be_sce, k=50){
## Split in two SCE objects
b1_be_sce <- be_sce[,be_sce$synth_batches=="B1"]
b2_be_sce <- be_sce[,be_sce$synth_batches=="B2"]
be_mnn <- reducedMNN(reducedDim(b1_be_sce, "pca.corrected"), reducedDim(b2_be_sce, "pca.corrected"), k=k)
reducedDim(be_sce, "pca.MNN") <- be_mnn$corrected[colnames(be_sce),]
be_sce
}
## Simulate batch effects of different magnitude
save_corrected_be <- function(pop, pop_enr, seed, be_sd){
## Load coldata and PCA
outdir <- '/nfs/team205/ed6/data/milo_benchmark/synthetic_data/'
outprefix <- str_c("benchmark_embryo_pop_", pop, '_enr', pop_enr, "_seed", seed)
coldata <- read_csv(paste0(outdir, outprefix, ".coldata.csv")) %>% column_to_rownames()
X_pca <-read_csv(str_c(outdir, outprefix, "_batchEffect", be_sd, ".pca.csv")) %>% column_to_rownames()
## Add reduced dim + coldata to sce
colData(embryo_sce) <- DataFrame(coldata)
reducedDim(embryo_sce, "pca_batch") <- as.matrix(X_pca)
set.seed(seed)
sce_be <- mnn_correct_batch_effect(embryo_sce)
X_pca <- reducedDim(sce_be, "pca.MNN")
## Save reduced dims
write_csv(as.data.frame(X_pca) %>% rownames_to_column(), str_c(outdir, outprefix, "_batchEffect", be_sd,".MNNcorrected.pca.csv"))
}
be_sds <- unique(out_meta_df$batchEffect)
for (pop in pops) {
for (seed in seeds) {
for (be_sd in c(0.5, 0.75, 1)) {
pop_enr = 0.7
save_corrected_be(pop, pop_enr, seed, be_sd)
}
}
}
Read all results
outdir <- "/nfs/team205/ed6/data/milo_benchmark/"
res_files <- list.files(outdir, pattern=".+MNNcorrected.+DAresults.+.csv")
res_files_full <- list.files(outdir, pattern=".+MNNcorrected.+DAresults.+.csv", full.names = TRUE)
# in_files <- list.files("~/data/milo_benchmark/synthetic_data/", pattern="_enr0.[78].+coldata.csv")
## Make data frame w benchmark parameters
mnn_out_meta_df <- data.frame(file_id = str_remove_all(res_files, "benchmark_embryo_pop_|.csv")) %>%
separate(col = file_id, sep = ".DAresults.", into=c("file_id", "method")) %>%
separate(col = file_id, sep = "_enr", into=c("pop", "file_id")) %>%
separate(col = file_id, sep = "_", into=c("enr", "seed", "batchEffect")) %>%
mutate(seed=str_remove(seed, "seed"), batchEffect=str_remove(batchEffect, "batchEffect"))
## Load results
mnn_outcome_df <- lapply(seq_along(res_files_full), function(i){
print(paste("Outcome no. ", i))
benchmark_df <- read_csv(res_files_full[i]) %>%
.outcome_by_prob(da_upper = 0.6) %>%
mutate(DA_thresh=0.6) %>%
ungroup() %>%
dplyr::select(- method) %>%
bind_cols(mnn_out_meta_df[i,])
pop_enr <- as.numeric(mnn_out_meta_df[i,"enr"])
benchmark_df
}) %>%
purrr::reduce(bind_rows)
mnn_outcome_df %>%
filter(method=="meld")
mnn_outcome_df %>%
mutate(batchEffect = as.numeric(str_remove(batchEffect, ".MNNcorrected"))) %>%
mutate(FDR = FP/(TP+FP)) %>%
mutate(TPR=ifelse(is.nan(TPR), 0, TPR),
FPR=ifelse(is.nan(FPR), 0, FPR),
FDR=ifelse(is.nan(FDR), 0, FDR),
Precision=ifelse(is.nan(Precision), 0, Precision)) %>%
rename(metric=metric) %>%
group_by(method, DA_thresh, batchEffect, enr) %>%
summarise(mean_metric=mean(metric),
sd_metric=sd(metric)) %>%
mutate(group=paste(method, enr)) %>%
ggplot(aes(batchEffect, mean_metric, color=method)) +
geom_line() +
geom_ribbon(aes(ymin=mean_metric-sd_metric, ymax=mean_metric+sd_metric, fill=method), alpha=0.2, color=NA) +
geom_point(size=3) +
facet_grid(enr~., labeller="label_both") +
xlab("Batch effect magnitude") +
ylab(paste("mean", metric)) +
theme_bw(base_size=16) +
scale_color_brewer(palette="Dark2") +
geom_hline(yintercept = 0.8, linetype=2) +
scale_fill_brewer(palette="Dark2")
`summarise()` regrouping output by 'method', 'DA_thresh', 'batchEffect' (override with `.groups` argument)
Plot UMAP
X_pca_mnn <- read_csv("/nfs/team205/ed6/data/milo_benchmark/synthetic_data/benchmark_embryo_pop_Somitic_mesoderm_enr0.8_seed43_batchEffect0.75.MNNcorrected.pca.csv") %>%
column_to_rownames()
all(rownames(X_pca_mnn) == colnames(embryo_sce))
coldata <- read_csv("/nfs/team205/ed6/data/milo_benchmark/synthetic_data/benchmark_embryo_pop_Somitic_mesoderm_enr0.8_seed43.coldata.csv")
colData(embryo_sce) <- coldata %>% column_to_rownames() %>% DataFrame()
reducedDim(embryo_sce, "pca_batch") <- as.matrix(X_pca_mnn)
embryo_sce <- runUMAP(embryo_sce, dimred="pca_batch", name="umap_batch")
indir <- "~/data/milo_benchmark/synthetic_data/"
res <- read_csv("/nfs/team205/ed6/data/milo_benchmark/benchmark_embryo_pop_Somitic_mesoderm_enr0.8_seed43_batchEffect0.75.DAresults.milo.csv")
embryo_sce$pred <- res$pred
embryo_sce$true <- res$true
plotReducedDim(embryo_sce, "umap_batch", colour_by="Condition1_prob", point_size=0.1)
plotReducedDim(embryo_sce, "umap_batch", colour_by="pred", point_size=0.1)
plotReducedDim(embryo_sce, "umap_batch", colour_by="true", point_size=0.1)
res %>%
.outcome_by_prob(da_upper = 0.6) %>%
mutate(FDR = FP/(TP+FP))
outdir <- "/nfs/team205/ed6/data/milo_benchmark/"
res_files <- list.files(outdir, pattern="linear.+DAresults")
res_files_full <- list.files(outdir, pattern="linear.+DAresults", full.names = TRUE)
in_files <- list.files("~/data/milo_benchmark/synthetic_data/", pattern="linear.+coldata.csv")
## Make data frame w benchmark parameters
linear_out_meta_df <- data.frame(file_id = str_remove_all(res_files, "benchmark_embryo_pop_|.csv")) %>%
separate(col = file_id, sep = ".DAresults.", into=c("file_id", "method")) %>%
separate(col = file_id, sep = "_enr", into=c("pop", "file_id")) %>%
separate(col = file_id, sep = "_", into=c("enr", "seed", "batchEffect")) %>%
mutate(seed=str_remove(seed, "seed"), batchEffect=str_remove(batchEffect, "batchEffect"))
## Load results
linear_outcome_df <- lapply(seq_along(res_files_full), function(i){
print(paste("Outcome no. ", i))
benchmark_df <- read_csv(res_files_full[i]) %>%
.outcome_by_prob(da_upper = 0.6) %>%
mutate(DA_thresh=0.6) %>%
ungroup() %>%
dplyr::select(- method) %>%
bind_cols(linear_out_meta_df[i,])
pop_enr <- as.numeric(linear_out_meta_df[i,"enr"])
benchmark_df
}) %>%
purrr::reduce(bind_rows)
[1] "Outcome no. 1"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 2"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 3"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 4"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 5"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 6"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 7"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 8"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 9"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 10"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 11"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 12"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 13"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 14"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 15"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 16"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 17"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 18"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 19"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 20"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 21"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 22"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 23"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 24"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 25"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 26"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 27"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 28"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 29"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 30"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 31"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 32"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 33"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 34"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 35"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 36"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 37"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 38"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 39"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 40"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 41"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 42"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 43"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 44"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 45"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 46"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 47"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 48"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 49"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 50"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 51"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 52"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 53"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 54"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 55"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 56"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 57"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 58"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 59"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 60"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 61"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 62"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 63"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 64"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 65"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 66"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 67"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 68"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 69"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 70"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 71"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 72"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 73"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 74"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 75"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 76"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 77"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 78"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 79"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 80"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 81"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 82"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 83"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 84"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 85"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 86"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 87"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 88"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 89"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 90"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 91"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 92"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 93"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 94"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 95"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 96"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 97"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 98"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 99"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 100"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 101"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 102"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 103"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 104"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 105"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 106"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 107"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 108"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 109"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 110"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 111"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 112"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 113"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 114"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 115"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 116"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 117"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 118"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 119"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 120"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 121"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 122"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 123"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 124"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 125"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 126"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 127"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 128"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 129"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 130"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 131"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 132"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 133"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 134"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 135"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 136"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 137"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 138"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 139"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 140"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 141"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 142"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 143"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 144"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 145"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 146"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 147"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 148"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 149"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 150"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 151"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 152"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 153"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 154"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 155"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 156"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 157"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 158"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 159"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 160"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 161"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 162"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 163"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 164"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 165"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 166"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 167"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 168"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 169"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 170"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 171"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 172"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 173"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 174"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 175"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 176"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 177"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 178"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 179"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 180"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 181"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 182"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 183"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 184"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 185"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 186"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 187"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 188"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 189"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 190"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 191"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 192"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 193"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 194"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 195"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 196"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 197"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 198"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 199"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 200"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 201"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 202"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 203"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 204"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 205"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 206"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 207"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 208"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 209"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 210"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 211"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 212"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 213"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 214"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 215"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 216"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 217"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 218"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 219"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 220"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 221"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 222"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 223"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 224"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 225"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 226"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 227"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 228"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 229"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 230"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 231"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 232"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 233"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 234"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 235"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 236"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 237"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 238"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 239"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 240"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 241"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 242"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 243"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 244"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 245"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 246"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 247"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 248"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 249"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 250"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 251"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 252"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 253"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 254"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 255"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 256"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 257"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 258"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 259"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 260"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 261"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 262"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 263"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 264"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 265"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 266"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 267"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 268"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 269"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 270"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 271"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 272"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 273"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 274"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 275"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 276"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 277"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 278"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 279"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 280"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 281"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 282"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 283"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 284"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 285"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 286"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 287"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 288"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 289"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 290"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 291"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 292"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 293"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 294"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 295"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 296"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 297"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 298"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 299"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 300"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 301"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 302"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 303"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 304"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 305"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 306"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 307"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 308"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 309"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 310"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 311"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 312"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 313"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 314"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 315"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 316"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 317"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 318"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 319"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 320"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 321"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 322"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 323"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 324"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 325"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 326"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 327"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 328"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 329"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 330"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 331"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 332"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 333"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 334"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 335"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 336"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 337"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 338"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 339"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 340"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 341"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 342"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 343"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 344"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 345"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 346"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 347"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 348"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 349"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 350"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 351"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 352"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 353"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 354"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 355"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 356"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 357"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
linear_outcome_df
pl_df <- linear_outcome_df %>%
filter(method!="milo_batch") %>%
mutate(FDR = FP/(TP+FP)) %>%
mutate(TPR=ifelse(is.nan(TPR), 0, TPR),
FPR=ifelse(is.nan(FPR), 0, FPR),
FDR=ifelse(is.nan(FDR), 0, FDR),
Precision=ifelse(is.nan(Precision), 0, Precision)) %>%
mutate(pop=str_replace(pop, "_", " ")) %>%
mutate(pop_size=pop_sizes[pop]) %>%
arrange(pop_size) %>%
mutate(pop=factor(pop, levels=unique(pop))) %>%
filter(DA_thresh >= 0.6 & batchEffect==0)
pl_right <- pl_df %>%
ggplot(aes(method, TPR, color=method)) +
geom_boxplot() +
ggbeeswarm::geom_quasirandom(alpha=0.5) +
geom_hline(yintercept = 0.8, linetype=2) +
theme_bw(base_size = 16) +
scale_color_brewer(palette="Dark2") +
facet_grid(enr~., labeller = 'label_both') +
pl_df %>%
ggplot(aes(method, FDR, color=method)) +
geom_boxplot() +
ggbeeswarm::geom_quasirandom(alpha=0.5) +
geom_hline(yintercept = 0.1, linetype=2) +
theme_bw(base_size = 16) +
scale_color_brewer(palette="Dark2") +
facet_grid(enr~., labeller = 'label_both') +
plot_layout(guides='collect') &
theme(axis.text.x=element_blank())
pl_right
Plot UMAP
linear_sce <- readRDS("~/data/milo_benchmark/linear_data_bm.RDS")
indir <- "~/data/milo_benchmark/synthetic_data/"
names(reducedDims(linear_sce))[2] <- "umap_batch"
colnames(reducedDims(linear_sce)[[2]]) <- c("X1", "X2")
pl_top <- plot_outcome_umap(linear_sce, method = 'milo', pop="M5", enr = 0.9, seed = 43,batchEffect = 0, true = TRUE, data_id="linear") + coord_fixed() + theme(legend.position="right")
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
celltype = [31mcol_character()[39m,
synth_labels = [31mcol_character()[39m,
synth_samples = [31mcol_character()[39m,
synth_batches = [31mcol_character()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m,
true_labels = [31mcol_character()[39m,
cell = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
plot_linear <- ((pl_top) / (pl_right)) +
plot_layout(heights = c(1,2))
plot_linear
outdir <- "/nfs/team205/ed6/data/milo_benchmark/"
res_files <- list.files(outdir, pattern="branching.+DAresults")
res_files_full <- list.files(outdir, pattern="branching.+DAresults", full.names = TRUE)
in_files <- list.files("~/data/milo_benchmark/synthetic_data/", pattern="branching.+coldata.csv")
## Make data frame w benchmark parameters
branch_out_meta_df <- data.frame(file_id = str_remove_all(res_files, "benchmark_embryo_pop_|.csv")) %>%
separate(col = file_id, sep = ".DAresults.", into=c("file_id", "method")) %>%
separate(col = file_id, sep = "_enr", into=c("pop", "file_id")) %>%
separate(col = file_id, sep = "_", into=c("enr", "seed", "batchEffect")) %>%
mutate(seed=str_remove(seed, "seed"), batchEffect=str_remove(batchEffect, "batchEffect"))
## Load results
branch_outcome_df <- lapply(seq_along(res_files_full), function(i){
print(paste("Outcome no. ", i))
benchmark_df <- read_csv(res_files_full[i]) %>%
.outcome_by_prob(da_upper = 0.6) %>%
mutate(DA_thresh=0.6) %>%
ungroup() %>%
dplyr::select(- method) %>%
bind_cols(branch_out_meta_df[i,])
pop_enr <- as.numeric(branch_out_meta_df[i,"enr"])
benchmark_df
}) %>%
purrr::reduce(bind_rows)
[1] "Outcome no. 1"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 2"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 3"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 4"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 5"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 6"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 7"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 8"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 9"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 10"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 11"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 12"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 13"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 14"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 15"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 16"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 17"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 18"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 19"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 20"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 21"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 22"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 23"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 24"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 25"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 26"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 27"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 28"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 29"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 30"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 31"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 32"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 33"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 34"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 35"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 36"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 37"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 38"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 39"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 40"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 41"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 42"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 43"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 44"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 45"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 46"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 47"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 48"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 49"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 50"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 51"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 52"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 53"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 54"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 55"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 56"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 57"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 58"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 59"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 60"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 61"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 62"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 63"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 64"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 65"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 66"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 67"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 68"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 69"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 70"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 71"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 72"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 73"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 74"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 75"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 76"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 77"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 78"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 79"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 80"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 81"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 82"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 83"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 84"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 85"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 86"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 87"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 88"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 89"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 90"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 91"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 92"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 93"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 94"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 95"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 96"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 97"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 98"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 99"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 100"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 101"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 102"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 103"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 104"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 105"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 106"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 107"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 108"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 109"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 110"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 111"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 112"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 113"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 114"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 115"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 116"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 117"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 118"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 119"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 120"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 121"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 122"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 123"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 124"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 125"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 126"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 127"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 128"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 129"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 130"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 131"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 132"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 133"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 134"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 135"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 136"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 137"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 138"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 139"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 140"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 141"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 142"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 143"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 144"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 145"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 146"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 147"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 148"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 149"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 150"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 151"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 152"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 153"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 154"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 155"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 156"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 157"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 158"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 159"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 160"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 161"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 162"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 163"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 164"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 165"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 166"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 167"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 168"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 169"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 170"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 171"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 172"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 173"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 174"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 175"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 176"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 177"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 178"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 179"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 180"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 181"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 182"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 183"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 184"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 185"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 186"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 187"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 188"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 189"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 190"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 191"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 192"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 193"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 194"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 195"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 196"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 197"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 198"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 199"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 200"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 201"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 202"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 203"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 204"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 205"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 206"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 207"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 208"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 209"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 210"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 211"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 212"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 213"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 214"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 215"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 216"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 217"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 218"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 219"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 220"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 221"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 222"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 223"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 224"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 225"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 226"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 227"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 228"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 229"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 230"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 231"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 232"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 233"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 234"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 235"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 236"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 237"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 238"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 239"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 240"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 241"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 242"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 243"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 244"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 245"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 246"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 247"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 248"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 249"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 250"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 251"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 252"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 253"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 254"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 255"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 256"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 257"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 258"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 259"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 260"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 261"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 262"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 263"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 264"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 265"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 266"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 267"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 268"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 269"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 270"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 271"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 272"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 273"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 274"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 275"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 276"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 277"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 278"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 279"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 280"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 281"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 282"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 283"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 284"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 285"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 286"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 287"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 288"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 289"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 290"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 291"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 292"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 293"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 294"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 295"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 296"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 297"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 298"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 299"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 300"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 301"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 302"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 303"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 304"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 305"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 306"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 307"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 308"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 309"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 310"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 311"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 312"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 313"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 314"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 315"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 316"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 317"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 318"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 319"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 320"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 321"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 322"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 323"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 324"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 325"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 326"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 327"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 328"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 329"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 330"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 331"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 332"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 333"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 334"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 335"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 336"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 337"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 338"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 339"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 340"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 341"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 342"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 343"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 344"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 345"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 346"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 347"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 348"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 349"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 350"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 351"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 352"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 353"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 354"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 355"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 356"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 357"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 358"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 359"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 360"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 361"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 362"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 363"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 364"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 365"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 366"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 367"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 368"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 369"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 370"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 371"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 372"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 373"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 374"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 375"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 376"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 377"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 378"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 379"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 380"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 381"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 382"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 383"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 384"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 385"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 386"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 387"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 388"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 389"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 390"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 391"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 392"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 393"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 394"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 395"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 396"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 397"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 398"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 399"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 400"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 401"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 402"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 403"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 404"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 405"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 406"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 407"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 408"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 409"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 410"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 411"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 412"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 413"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 414"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 415"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 416"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 417"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 418"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 419"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 420"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 421"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 422"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 423"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 424"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 425"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 426"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 427"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 428"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 429"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 430"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 431"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 432"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 433"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 434"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 435"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 436"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 437"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 438"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 439"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 440"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 441"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 442"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 443"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 444"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 445"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 446"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 447"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 448"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 449"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 450"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 451"
[36m──[39m [1m[1mColumn specification[1m[22m [36m──────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
branch_outcome_df
NA
pl_df <- branch_outcome_df %>%
filter(method!="milo_batch") %>%
mutate(FDR = FP/(TP+FP)) %>%
mutate(TPR=ifelse(is.nan(TPR), 0, TPR),
FPR=ifelse(is.nan(FPR), 0, FPR),
FDR=ifelse(is.nan(FDR), 0, FDR),
Precision=ifelse(is.nan(Precision), 0, Precision)) %>%
mutate(pop=str_replace(pop, "_", " ")) %>%
mutate(pop_size=pop_sizes[pop]) %>%
arrange(pop_size) %>%
mutate(pop=factor(pop, levels=unique(pop))) %>%
filter(DA_thresh >= 0.6 & batchEffect==0)
pl_right <- pl_df %>%
ggplot(aes(method, TPR, color=method)) +
geom_boxplot() +
ggbeeswarm::geom_quasirandom(alpha=0.5) +
geom_hline(yintercept = 0.8, linetype=2) +
theme_bw(base_size = 16) +
scale_color_brewer(palette="Dark2") +
facet_grid(enr~., labeller = 'label_both') +
pl_df %>%
ggplot(aes(method, FDR, color=method)) +
geom_boxplot() +
ggbeeswarm::geom_quasirandom(alpha=0.5) +
geom_hline(yintercept = 0.1, linetype=2) +
theme_bw(base_size = 16) +
scale_color_brewer(palette="Dark2") +
facet_grid(enr~., labeller = 'label_both') +
plot_layout(guides='collect') &
theme(axis.text.x=element_blank())
pl_right
Plot UMAP
branch_sce <- readRDS("~/data/milo_benchmark/branching_data_bm.RDS")
indir <- "~/data/milo_benchmark/synthetic_data/"
names(reducedDims(branch_sce))[2] <- "umap_batch"
colnames(reducedDims(branch_sce)[[2]]) <- c("X1", "X2")
pl_top <- plot_outcome_umap(branch_sce, method = 'milo', pop="M5", enr = 0.9, seed = 43,batchEffect = 0, true = TRUE, data_id="branching") + coord_fixed() + theme(legend.position="right")
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
celltype = [31mcol_character()[39m,
synth_labels = [31mcol_character()[39m,
synth_samples = [31mcol_character()[39m,
synth_batches = [31mcol_character()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m,
true_labels = [31mcol_character()[39m,
cell = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
plot_branch <- ((pl_top) / (pl_right)) +
plot_layout(heights = c(1,2))
plot_branch
outdir <- "/nfs/team205/ed6/data/milo_benchmark/"
res_files <- list.files(outdir, pattern="cluster.+DAresults")
res_files_full <- list.files(outdir, pattern="cluster.+DAresults", full.names = TRUE)
in_files <- list.files("~/data/milo_benchmark/synthetic_data/", pattern="cluster.+coldata.csv")
## Make data frame w benchmark parameters
cluster_out_meta_df <- data.frame(file_id = str_remove_all(res_files, "benchmark_embryo_pop_|.csv")) %>%
separate(col = file_id, sep = ".DAresults.", into=c("file_id", "method")) %>%
separate(col = file_id, sep = "_enr", into=c("pop", "file_id")) %>%
separate(col = file_id, sep = "_", into=c("enr", "seed", "batchEffect")) %>%
mutate(seed=str_remove(seed, "seed"), batchEffect=str_remove(batchEffect, "batchEffect"))
## Load results
cluster_outcome_df <- lapply(seq_along(res_files_full), function(i){
print(paste("Outcome no. ", i))
benchmark_df <- read_csv(res_files_full[i]) %>%
.outcome_by_prob(da_upper = 0.6) %>%
mutate(DA_thresh=0.6) %>%
ungroup() %>%
dplyr::select(- method) %>%
bind_cols(cluster_out_meta_df[i,])
pop_enr <- as.numeric(cluster_out_meta_df[i,"enr"])
benchmark_df
}) %>%
purrr::reduce(bind_rows)
[1] "Outcome no. 1"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 2"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 3"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 4"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 5"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 6"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 7"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 8"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 9"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 10"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 11"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 12"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 13"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 14"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 15"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 16"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 17"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 18"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 19"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 20"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 21"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 22"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 23"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 24"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 25"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 26"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 27"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 28"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 29"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 30"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 31"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 32"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 33"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 34"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 35"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 36"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 37"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 38"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 39"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 40"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 41"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 42"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 43"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 44"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 45"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 46"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 47"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 48"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 49"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 50"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 51"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 52"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 53"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 54"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 55"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 56"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 57"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 58"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 59"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 60"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 61"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 62"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 63"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 64"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 65"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 66"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 67"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 68"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 69"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 70"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 71"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 72"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 73"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 74"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 75"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 76"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 77"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 78"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 79"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 80"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 81"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 82"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 83"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 84"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 85"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 86"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 87"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 88"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 89"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 90"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 91"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 92"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 93"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 94"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 95"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 96"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 97"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 98"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 99"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 100"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 101"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 102"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 103"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 104"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 105"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 106"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 107"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 108"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 109"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 110"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 111"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 112"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 113"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 114"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 115"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 116"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 117"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 118"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 119"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 120"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 121"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 122"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 123"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 124"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 125"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 126"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 127"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 128"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 129"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 130"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 131"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 132"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 133"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 134"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 135"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 136"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 137"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 138"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 139"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 140"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 141"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 142"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 143"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 144"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 145"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 146"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 147"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 148"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 149"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 150"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 151"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 152"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 153"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
the standard deviation is zero`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 154"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 155"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 156"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 157"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 158"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 159"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 160"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Condition2 = [32mcol_double()[39m,
method = [31mcol_character()[39m,
true = [31mcol_character()[39m,
true_prob = [32mcol_double()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 161"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
[1] "Outcome no. 162"
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
cluster_outcome_df
NA
pl_df <- cluster_outcome_df %>%
filter(method!="milo_batch") %>%
mutate(FDR = FP/(TP+FP)) %>%
mutate(TPR=ifelse(is.nan(TPR), 0, TPR),
FPR=ifelse(is.nan(FPR), 0, FPR),
FDR=ifelse(is.nan(FDR), 0, FDR),
Precision=ifelse(is.nan(Precision), 0, Precision)) %>%
mutate(pop=str_replace(pop, "_", " ")) %>%
mutate(pop_size=pop_sizes[pop]) %>%
arrange(pop_size) %>%
mutate(pop=factor(pop, levels=unique(pop))) %>%
filter(DA_thresh >= 0.6 & batchEffect==0)
pl_right <- pl_df %>%
ggplot(aes(method, TPR, color=method)) +
geom_boxplot() +
ggbeeswarm::geom_quasirandom(alpha=0.5) +
geom_hline(yintercept = 0.8, linetype=2) +
theme_bw(base_size = 16) +
scale_color_brewer(palette="Dark2") +
facet_grid(enr~., labeller = 'label_both') +
pl_df %>%
ggplot(aes(method, FDR, color=method)) +
geom_boxplot() +
ggbeeswarm::geom_quasirandom(alpha=0.5) +
geom_hline(yintercept = 0.1, linetype=2) +
theme_bw(base_size = 16) +
scale_color_brewer(palette="Dark2") +
facet_grid(enr~., labeller = 'label_both') +
plot_layout(guides='collect') &
theme(axis.text.x=element_blank())
pl_right
Plot UMAP
cluster_sce <- readRDS("~/data/milo_benchmark/cluster_data_bm.RDS")
indir <- "~/data/milo_benchmark/synthetic_data/"
names(reducedDims(cluster_sce))[2] <- "umap_batch"
colnames(reducedDims(cluster_sce)[[2]]) <- c("X1", "X2")
pl_top <- plot_outcome_umap(cluster_sce, method = 'milo', pop="3", enr = 0.9, seed = 43,batchEffect = 0, true = TRUE, data_id="cluster") + theme(legend.position="right")
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
rowname = [31mcol_character()[39m,
Block = [31mcol_character()[39m,
Replicate = [31mcol_character()[39m,
synth_labels = [31mcol_character()[39m,
synth_samples = [31mcol_character()[39m,
synth_batches = [31mcol_character()[39m,
Condition1_prob = [32mcol_double()[39m,
Condition2_prob = [32mcol_double()[39m,
true_labels = [31mcol_character()[39m,
celltype = [32mcol_double()[39m,
cell = [31mcol_character()[39m
)
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
plot_cluster <- ((pl_top) / (pl_right)) +
plot_layout(heights = c(1,2))
plot_cluster
Pick radius parameter for different datasets: For each hypersphere centred on a cell, the radius required to include a certain number of nearest neighbours is computed.
sample_col = "synth_samples"
d=30
sce <- readRDS("/nfs/team205/ed6/data/milo_benchmark/cluster_data_bm.RDS")
sample_ls <- split(1:ncol(sce), sce[[sample_col]])
processed.exprs <- lapply(sample_ls, function(s) reducedDim(sce[,s], "pca.corrected")[,1:d])
cd <- prepareCellData(processed.exprs)
boxplot(neighborDistances(cd, neighbors=50, as.tol=TRUE))
unique(sce$celltype)
[1] "M3" "M8" "M5" "M2" "M1" "M4" "M7" "M6" "M10" "M9"
unique(sce$celltype)
[1] "M4" "M6" "M2" "M5" "M1" "M7" "M3"
sample_col = "synth_samples"
d=30
sce <- embryo_sce
sample_ls <- split(1:ncol(sce), sce[[sample_col]])
processed.exprs <- lapply(sample_ls, function(s) reducedDim(sce[,s], "pca.corrected")[,1:d])
cd <- prepareCellData(processed.exprs)
boxplot(neighborDistances(cd, neighbors=50, as.tol=TRUE))
cydar_res <- runDA(sce, coldata, X_pca, "cydar", out_type = "labels",d = 30,
params=list(cydar=list(tol=tol_dataset[[data_id]], downsample=3)))
df <- .outcome_by_prob(cydar_res, 0.6)
the condition has length > 1 and only the first element will be used`summarise()` regrouping output by 'method' (override with `.groups` argument)
df
sim.design <- model.matrix(design, data=design_df)[colnames(cd.dge),]
sim.dge <- estimateDisp(cd.dge, sim.design)
sim.fit <- glmQLFit(sim.dge, sim.design)
sim.res <- glmQLFTest(sim.fit, coef=2)
# control the spatial FDR
cydar.res <- sim.res$table
cydar.res$SpatialFDR <- spatialFDR(intensities(cd), sim.res$table$PValue)
boxplot(sim.dge$counts)
hist(cydar.res$PValue)
cydar.res$SpatialFDR < 0.1
Try downsampling one of the 2 conditions
sce
class: SingleCellExperiment
dim: 100 900
metadata(0):
assays(1): logcounts
rownames(100): Gene1 Gene2 ... Gene99 Gene100
rowData names(0):
colnames(900): Cell1 Cell2 ... Cell899 Cell900
colData names(10): Block Replicate ... celltype cell
reducedDimNames(3): pca.corrected UMAP pca_batch
altExpNames(0):
benchmark_df <- read_csv("/nfs/team205/ed6/data/milo_benchmark/benchmark_embryo_pop_Erythroid2_enr0.9_seed43_batchEffect0.DAresults.daseq.csv")
[36m──[39m [1m[1mColumn specification[1m[22m [36m───────────────────────────────────────────────────[39m
cols(
true_prob = [32mcol_double()[39m,
true = [31mcol_character()[39m,
method = [31mcol_character()[39m,
pred = [31mcol_character()[39m
)
.outcome_by_prob(benchmark_df, 0.6)
`summarise()` regrouping output by 'method' (override with `.groups` argument)
## Check if conditions were swapped in test
pred_cor <- benchmark_df %>%
mutate(pred=factor(pred, levels=c("NegLFC", "NotDA", "PosLFC"), ordered = TRUE)) %>%
mutate(pred=as.numeric(pred)) %>%
summarise(cor(pred, true_prob))
if (pred_cor < 0) {
benchmark_df <- mutate(benchmark_df, pred = ifelse(pred=="NegLFC", "PosLFC", ifelse(pred=="PosLFC", "NegLFC", "NotDA")))
}
.outcome_by_prob(benchmark_df, 0.6)
`summarise()` regrouping output by 'method' (override with `.groups` argument)